Overview

Dataset statistics

Number of variables12
Number of observations760552
Missing cells0
Missing cells (%)0.0%
Duplicate rows1161
Duplicate rows (%)0.2%
Total size in memory69.6 MiB
Average record size in memory96.0 B

Variable types

Numeric11
Categorical1

Alerts

Dataset has 1161 (0.2%) duplicate rowsDuplicates
dropped_frames_mean is highly correlated with dropped_frames_std and 1 other fieldsHigh correlation
dropped_frames_max is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
bitrate_mean is highly correlated with bitrate_std and 1 other fieldsHigh correlation
y is highly correlated with rtt_mean and 1 other fieldsHigh correlation
rtt_mean is highly correlated with yHigh correlation
dropped_frames_std is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
bitrate_std is highly correlated with bitrate_meanHigh correlation
packet_loss_rate is highly correlated with packet_loss_stdHigh correlation
packet_loss_std is highly correlated with packet_loss_rateHigh correlation
fps_std has 107380 (14.1%) zeros Zeros
rtt_std has 13284 (1.7%) zeros Zeros
dropped_frames_std has 673271 (88.5%) zeros Zeros
packet_loss_std has 456234 (60.0%) zeros Zeros

Reproduction

Analysis started2022-11-02 13:56:40.499081
Analysis finished2022-11-02 13:57:57.408883
Duration1 minute and 16.91 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

fps_mean
Real number (ℝ≥0)

Distinct189275
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5756194107
Minimum9.174227759 × 10-6
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:57.562550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.174227759 × 10-6
5-th percentile0.2965042904
Q10.4812759362
median0.5625768755
Q30.6771102408
95-th percentile0.8740768724
Maximum1
Range0.9999908258
Interquartile range (IQR)0.1958343046

Descriptive statistics

Standard deviation0.1703089136
Coefficient of variation (CV)0.2958706924
Kurtosis0.4472028681
Mean0.5756194107
Median Absolute Deviation (MAD)0.09825289865
Skewness0.008931729779
Sum437788.4941
Variance0.02900512606
MonotonicityNot monotonic
2022-11-02T16:57:57.696218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110905
 
1.4%
0.50024987515731
 
0.8%
0.50012496885233
 
0.7%
0.57148978724631
 
0.6%
0.50008331944628
 
0.6%
0.66677774084530
 
0.6%
0.55560493284357
 
0.6%
0.75006248443446
 
0.5%
0.6000799843444
 
0.5%
0.50006249222875
 
0.4%
Other values (189265)710772
93.5%
ValueCountFrequency (%)
9.174227759 × 10-61
 
< 0.1%
1.020397751 × 10-53
 
< 0.1%
1.075257255 × 10-51
 
< 0.1%
1.136350723 × 10-52
 
< 0.1%
1.176456748 × 10-53
 
< 0.1%
1.204804761 × 10-53
 
< 0.1%
1.219497323 × 10-51
 
< 0.1%
1.369844249 × 10-51
 
< 0.1%
1.388869599 × 10-53
 
< 0.1%
1.428551021 × 10-520
< 0.1%
ValueCountFrequency (%)
110905
1.4%
0.99995385253
 
< 0.1%
0.9999077052
 
< 0.1%
0.99960067391
 
< 0.1%
0.99959769619
 
< 0.1%
0.99958020461
 
< 0.1%
0.999574012715
 
< 0.1%
0.99955507684
 
< 0.1%
0.99954362731
 
< 0.1%
0.99951462921
 
< 0.1%

fps_std
Real number (ℝ≥0)

ZEROS

Distinct300062
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08429972315
Minimum0
Maximum0.5102436748
Zeros107380
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:57.838310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02655204829
median0.07009030217
Q30.1273454726
95-th percentile0.2205730416
Maximum0.5102436748
Range0.5102436748
Interquartile range (IQR)0.1007934243

Descriptive statistics

Standard deviation0.07194858304
Coefficient of variation (CV)0.8534854013
Kurtosis0.8895205927
Mean0.08429972315
Median Absolute Deviation (MAD)0.04870291886
Skewness0.9866873507
Sum64114.32304
Variance0.005176598602
MonotonicityNot monotonic
2022-11-02T16:57:57.972583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0107380
 
14.1%
0.029156426972141
 
0.3%
0.03401502172140
 
0.3%
0.040816665711873
 
0.2%
0.025512329111720
 
0.2%
0.020410373491692
 
0.2%
0.10201106711514
 
0.2%
0.018555053651334
 
0.2%
0.051018281741329
 
0.2%
0.017008928031325
 
0.2%
Other values (300052)638104
83.9%
ValueCountFrequency (%)
0107380
14.1%
4.115434055 × 10-51
 
< 0.1%
4.929235329 × 10-51
 
< 0.1%
4.929235329 × 10-51
 
< 0.1%
4.929235329 × 10-51
 
< 0.1%
5.798256617 × 10-51
 
< 0.1%
6.604926637 × 10-51
 
< 0.1%
6.604926637 × 10-51
 
< 0.1%
6.81776414 × 10-51
 
< 0.1%
6.81776414 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.51024367481
 
< 0.1%
0.51024367482
< 0.1%
0.50880777511
 
< 0.1%
0.50846890883
< 0.1%
0.50846890882
< 0.1%
0.50310704814
< 0.1%
0.50310704814
< 0.1%
0.49935754011
 
< 0.1%
0.49608699851
 
< 0.1%
0.49502211971
 
< 0.1%

rtt_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct373318
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2353769502
Minimum8.172604759 × 10-8
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:58.113174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8.172604759 × 10-8
5-th percentile0.02499294697
Q10.1000163633
median0.2008302276
Q30.3417324934
95-th percentile0.5440793205
Maximum1
Range0.9999999183
Interquartile range (IQR)0.2417161301

Descriptive statistics

Standard deviation0.1693772437
Coefficient of variation (CV)0.7195999587
Kurtosis0.6943592975
Mean0.2353769502
Median Absolute Deviation (MAD)0.1147317282
Skewness0.9201028354
Sum179016.4103
Variance0.02868865067
MonotonicityNot monotonic
2022-11-02T16:57:58.253731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3335554815623
 
0.1%
0.266813304555
 
0.1%
0.3230858952550
 
0.1%
0.241818303541
 
0.1%
0.312671832540
 
0.1%
0.2814296426540
 
0.1%
0.2501874531532
 
0.1%
0.3752082639532
 
0.1%
0.233486636530
 
0.1%
0.2918437057530
 
0.1%
Other values (373308)755079
99.3%
ValueCountFrequency (%)
8.172604759 × 10-82
 
< 0.1%
9.417081701 × 10-83
< 0.1%
1.050420058 × 10-71
 
< 0.1%
1.080146783 × 10-72
 
< 0.1%
1.162249981 × 10-75
< 0.1%
1.315789301 × 10-72
 
< 0.1%
1.328021072 × 10-71
 
< 0.1%
1.396062907 × 10-71
 
< 0.1%
1.420454344 × 10-73
< 0.1%
1.435131826 × 10-73
< 0.1%
ValueCountFrequency (%)
1372
< 0.1%
0.99816360118
 
< 0.1%
0.99799665581
 
< 0.1%
0.99790125841
 
< 0.1%
0.99632720224
 
< 0.1%
0.99616751531
 
< 0.1%
0.99453886151
 
< 0.1%
0.99160503361
 
< 0.1%
0.99040658271
 
< 0.1%
0.98981487141
 
< 0.1%

rtt_std
Real number (ℝ≥0)

ZEROS

Distinct472091
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08822453861
Minimum0
Maximum0.5102436748
Zeros13284
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:58.388954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004425442045
Q10.02938238466
median0.07297180067
Q30.1342989208
95-th percentile0.2187923301
Maximum0.5102436748
Range0.5102436748
Interquartile range (IQR)0.1049165362

Descriptive statistics

Standard deviation0.07006308835
Coefficient of variation (CV)0.7941451376
Kurtosis0.4551562002
Mean0.08822453861
Median Absolute Deviation (MAD)0.04947155498
Skewness0.8770441361
Sum67099.34929
Variance0.004908836349
MonotonicityNot monotonic
2022-11-02T16:57:58.513925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013284
 
1.7%
0.1604609838492
 
0.1%
0.1411296432476
 
0.1%
0.1696025917455
 
0.1%
0.1458615534443
 
0.1%
0.1547169682440
 
0.1%
0.1160473935439
 
0.1%
0.1881571818391
 
0.1%
0.1176224687388
 
0.1%
0.1017751205377
 
< 0.1%
Other values (472081)743367
97.7%
ValueCountFrequency (%)
013284
1.7%
4.352326262 × 10-51
 
< 0.1%
4.842922668 × 10-51
 
< 0.1%
5.499164317 × 10-51
 
< 0.1%
5.925227459 × 10-51
 
< 0.1%
6.345572749 × 10-51
 
< 0.1%
6.345572749 × 10-51
 
< 0.1%
6.487345206 × 10-51
 
< 0.1%
6.499444067 × 10-51
 
< 0.1%
6.581158214 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.51024367482
 
< 0.1%
0.51024367482
 
< 0.1%
0.50879231151
 
< 0.1%
0.50846890889
< 0.1%
0.50846890884
< 0.1%
0.50823436951
 
< 0.1%
0.50658596691
 
< 0.1%
0.50636343841
 
< 0.1%
0.50414371861
 
< 0.1%
0.50310704817
< 0.1%

dropped_frames_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct48925
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3826617612
Minimum2.212384486 × 10-6
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:58.654521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.212384486 × 10-6
5-th percentile4.166493063 × 10-5
Q10.001971333254
median0.02612516228
Q31
95-th percentile1
Maximum1
Range0.9999977876
Interquartile range (IQR)0.9980286667

Descriptive statistics

Standard deviation0.4735762482
Coefficient of variation (CV)1.237584458
Kurtosis-1.709015864
Mean0.3826617612
Median Absolute Deviation (MAD)0.02606960982
Skewness0.5217607138
Sum291034.1678
Variance0.2242744628
MonotonicityNot monotonic
2022-11-02T16:57:58.779486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1279210
36.7%
0.00099900099924762
 
3.3%
0.00011109876686367
 
0.8%
7.691716022 × 10-56346
 
0.8%
0.00016663889356207
 
0.8%
9.9990001 × 10-56200
 
0.8%
0.00014283673765954
 
0.8%
0.00024993751565856
 
0.8%
8.332638947 × 10-55841
 
0.8%
0.0001249843775824
 
0.8%
Other values (48915)407985
53.6%
ValueCountFrequency (%)
2.212384486 × 10-642
< 0.1%
2.5510139 × 10-63
 
< 0.1%
3.816779325 × 10-685
< 0.1%
4.273486011 × 10-623
 
< 0.1%
4.366793158 × 10-624
 
< 0.1%
4.975099626 × 10-648
< 0.1%
5.102014786 × 10-69
 
< 0.1%
5.235574683 × 10-616
 
< 0.1%
5.319120643 × 10-63
 
< 0.1%
5.714253061 × 10-63
 
< 0.1%
ValueCountFrequency (%)
1279210
36.7%
0.98550745641
 
< 0.1%
0.98412723613
 
< 0.1%
0.98076960062
 
< 0.1%
0.97777827163
 
< 0.1%
0.97752222561
 
< 0.1%
0.97697398721
 
< 0.1%
0.97587751041
 
< 0.1%
0.9753292723
 
< 0.1%
0.97478103361
 
< 0.1%

dropped_frames_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct45832
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01362011535
Minimum0
Maximum0.5107506233
Zeros673271
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:58.904456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1413058462
Maximum0.5107506233
Range0.5107506233
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05307309214
Coefficient of variation (CV)3.896669799
Kurtosis20.31024362
Mean0.01362011535
Median Absolute Deviation (MAD)0
Skewness4.432870549
Sum10358.80597
Variance0.002816753109
MonotonicityNot monotonic
2022-11-02T16:57:59.045052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0673271
88.5%
0.203920225212
 
< 0.1%
0.20407312775
 
< 0.1%
0.204101467374
 
< 0.1%
0.0133123724571
 
< 0.1%
0.204022134271
 
< 0.1%
0.204056126570
 
< 0.1%
0.204083328670
 
< 0.1%
0.204103734969
 
< 0.1%
0.204108444668
 
< 0.1%
Other values (45822)86501
 
11.4%
ValueCountFrequency (%)
0673271
88.5%
0.0013329544061
 
< 0.1%
0.0016284795651
 
< 0.1%
0.0016869624652
 
< 0.1%
0.0016942185541
 
< 0.1%
0.0017010203681
 
< 0.1%
0.0017992961031
 
< 0.1%
0.0018225207381
 
< 0.1%
0.0018617192171
 
< 0.1%
0.0020419491051
 
< 0.1%
ValueCountFrequency (%)
0.51075062331
< 0.1%
0.51052450871
< 0.1%
0.51041795461
< 0.1%
0.51038568261
< 0.1%
0.51034436021
< 0.1%
0.51029538161
< 0.1%
0.50975783391
< 0.1%
0.50897207591
< 0.1%
0.50897109411
< 0.1%
0.50880777511
< 0.1%

dropped_frames_max
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14085
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4327704281
Minimum2.212384486 × 10-6
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:59.169981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.212384486 × 10-6
5-th percentile4.166493063 × 10-5
Q10.002502159936
median0.05287256222
Q31
95-th percentile1
Maximum1
Range0.9999977876
Interquartile range (IQR)0.9974978401

Descriptive statistics

Standard deviation0.4783781229
Coefficient of variation (CV)1.105385424
Kurtosis-1.87550861
Mean0.4327704281
Median Absolute Deviation (MAD)0.05283089729
Skewness0.3021341801
Sum329144.4146
Variance0.2288456285
MonotonicityNot monotonic
2022-11-02T16:57:59.294955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300040
39.5%
0.00099900099924762
 
3.3%
0.00011109876686367
 
0.8%
7.691716022 × 10-56346
 
0.8%
9.9990001 × 10-56339
 
0.8%
0.00016663889356207
 
0.8%
0.00014283673765954
 
0.8%
0.00024993751565856
 
0.8%
8.332638947 × 10-55841
 
0.8%
0.0001249843775824
 
0.8%
Other values (14075)387016
50.9%
ValueCountFrequency (%)
2.212384486 × 10-642
< 0.1%
2.5510139 × 10-63
 
< 0.1%
3.816779325 × 10-685
< 0.1%
4.273486011 × 10-623
 
< 0.1%
4.366793158 × 10-624
 
< 0.1%
4.975099626 × 10-648
< 0.1%
5.102014786 × 10-69
 
< 0.1%
5.235574683 × 10-616
 
< 0.1%
5.319120643 × 10-63
 
< 0.1%
5.714253061 × 10-63
 
< 0.1%
ValueCountFrequency (%)
1300040
39.5%
0.9955752313
 
< 0.1%
0.99502490041
 
< 0.1%
0.99468087941
 
< 0.1%
0.99290785171
 
< 0.1%
0.99173560551
 
< 0.1%
0.99166673616
 
< 0.1%
0.99145306451
 
< 0.1%
0.99137938475
 
< 0.1%
0.99115052081
 
< 0.1%

bitrate_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct751876
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3954155218
Minimum1.965486026 × 10-8
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:59.435548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.965486026 × 10-8
5-th percentile0.04358780066
Q10.2623989517
median0.4515029172
Q30.5326837074
95-th percentile0.6218085872
Maximum1
Range0.9999999803
Interquartile range (IQR)0.2702847557

Descriptive statistics

Standard deviation0.1828442457
Coefficient of variation (CV)0.4624103902
Kurtosis-0.5967533
Mean0.3954155218
Median Absolute Deviation (MAD)0.106591225
Skewness-0.5614928049
Sum300734.0659
Variance0.03343201819
MonotonicityNot monotonic
2022-11-02T16:57:59.560515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1337
 
< 0.1%
0.250519405498
 
< 0.1%
0.379242379282
 
< 0.1%
0.471678550974
 
< 0.1%
0.167328104171
 
< 0.1%
0.178493651438
 
< 0.1%
0.226003659236
 
< 0.1%
0.00961123997535
 
< 0.1%
2.890006274 × 10-829
 
< 0.1%
0.0890120205229
 
< 0.1%
Other values (751866)759723
99.9%
ValueCountFrequency (%)
1.965486026 × 10-83
 
< 0.1%
2.201043246 × 10-81
 
< 0.1%
2.233189615 × 10-81
 
< 0.1%
2.460750961 × 10-83
 
< 0.1%
2.659362214 × 10-81
 
< 0.1%
2.681755942 × 10-81
 
< 0.1%
2.681755942 × 10-81
 
< 0.1%
2.832219247 × 10-81
 
< 0.1%
2.890006274 × 10-829
< 0.1%
2.890006274 × 10-85
 
< 0.1%
ValueCountFrequency (%)
1337
< 0.1%
0.99555697759
 
< 0.1%
0.99462015351
 
< 0.1%
0.99283344371
 
< 0.1%
0.97778962462
 
< 0.1%
0.97682777791
 
< 0.1%
0.97579360331
 
< 0.1%
0.97368564081
 
< 0.1%
0.97285080241
 
< 0.1%
0.97117754911
 
< 0.1%

bitrate_std
Real number (ℝ≥0)

HIGH CORRELATION

Distinct752256
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1132008756
Minimum0
Maximum0.5035953569
Zeros5560
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:59.701108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003524224267
Q10.06072879855
median0.1097045264
Q30.1606726634
95-th percentile0.2374695742
Maximum0.5035953569
Range0.5035953569
Interquartile range (IQR)0.09994386489

Descriptive statistics

Standard deviation0.07136567246
Coefficient of variation (CV)0.6304339265
Kurtosis-0.2021096572
Mean0.1132008756
Median Absolute Deviation (MAD)0.04998019806
Skewness0.3878603771
Sum86095.15231
Variance0.005093059206
MonotonicityNot monotonic
2022-11-02T16:57:59.833358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05560
 
0.7%
0.000715860283929
 
< 0.1%
0.00431037575325
 
< 0.1%
0.000708311458323
 
< 0.1%
0.0133937890518
 
< 0.1%
0.00515773550817
 
< 0.1%
0.000203184500815
 
< 0.1%
0.000304234425714
 
< 0.1%
0.0548595086214
 
< 0.1%
0.000567421193213
 
< 0.1%
Other values (752246)754824
99.2%
ValueCountFrequency (%)
05560
0.7%
7.119832657 × 10-61
 
< 0.1%
1.033153441 × 10-51
 
< 0.1%
1.147280428 × 10-51
 
< 0.1%
1.180477072 × 10-51
 
< 0.1%
1.43668448 × 10-51
 
< 0.1%
1.476011943 × 10-51
 
< 0.1%
1.476011943 × 10-51
 
< 0.1%
1.483783749 × 10-51
 
< 0.1%
1.736312689 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.50359535691
< 0.1%
0.50263941531
< 0.1%
0.47534764491
< 0.1%
0.47358062251
< 0.1%
0.47260506811
< 0.1%
0.46971831011
< 0.1%
0.46787890071
< 0.1%
0.46414656741
< 0.1%
0.46254296681
< 0.1%
0.46189143891
< 0.1%

packet_loss_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct95422
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2147650917
Minimum9.999900001 × 10-6
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:57:59.966514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.999900001 × 10-6
5-th percentile0.0001666388935
Q10.000999000999
median0.04198600467
Q30.1502463054
95-th percentile1
Maximum1
Range0.9999900001
Interquartile range (IQR)0.1492473044

Descriptive statistics

Standard deviation0.3622230366
Coefficient of variation (CV)1.686601084
Kurtosis0.7630554756
Mean0.2147650917
Median Absolute Deviation (MAD)0.04148625454
Skewness1.607374047
Sum163340.02
Variance0.1312055283
MonotonicityNot monotonic
2022-11-02T16:58:00.091445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1126937
 
16.7%
0.00099900099961011
 
8.0%
0.000499750124938914
 
5.1%
0.000333222259226454
 
3.5%
0.000249937515621114
 
2.8%
0.00019996000814542
 
1.9%
0.042624042629127
 
1.2%
0.00016663889358969
 
1.2%
0.00014283673766708
 
0.9%
0.0213226726469
 
0.9%
Other values (95412)440307
57.9%
ValueCountFrequency (%)
9.999900001 × 10-65
 
< 0.1%
1.086944707 × 10-53
 
< 0.1%
1.123582881 × 10-534
< 0.1%
1.136350723 × 10-52
 
< 0.1%
1.149412076 × 10-54
 
< 0.1%
1.204804761 × 10-52
 
< 0.1%
1.204804761 × 10-519
< 0.1%
1.219497323 × 10-52
 
< 0.1%
1.23455266 × 10-56
 
< 0.1%
1.249984375 × 10-59
 
< 0.1%
ValueCountFrequency (%)
1126937
16.7%
0.99555561481
 
< 0.1%
0.97777802471
 
< 0.1%
0.96875781051
 
< 0.1%
0.96250041671
 
< 0.1%
0.95956257041
 
< 0.1%
0.95837495841
 
< 0.1%
0.9506585441
 
< 0.1%
0.92560055231
 
< 0.1%
0.92405159432
 
< 0.1%

packet_loss_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct117927
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05316569909
Minimum0
Maximum0.5106262619
Zeros456234
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2022-11-02T16:58:00.232075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.09324115671
95-th percentile0.232131036
Maximum0.5106262619
Range0.5106262619
Interquartile range (IQR)0.09324115671

Descriptive statistics

Standard deviation0.08621962757
Coefficient of variation (CV)1.621715299
Kurtosis1.386590603
Mean0.05316569909
Median Absolute Deviation (MAD)0
Skewness1.542696074
Sum40435.27877
Variance0.007433824179
MonotonicityNot monotonic
2022-11-02T16:58:00.357048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0456234
60.0%
0.2039202257134
 
0.9%
0.10201106716469
 
0.9%
0.068018708844962
 
0.7%
0.051018281743670
 
0.5%
0.20402213423087
 
0.4%
0.040816665712551
 
0.3%
0.28204780352419
 
0.3%
0.13603741772268
 
0.3%
0.2039202251965
 
0.3%
Other values (117917)269793
35.5%
ValueCountFrequency (%)
0456234
60.0%
0.0018420977871
 
< 0.1%
0.0019207556522
 
< 0.1%
0.0021888738982
 
< 0.1%
0.0021988742971
 
< 0.1%
0.0022187165933
 
< 0.1%
0.0023719078081
 
< 0.1%
0.0023993746364
 
< 0.1%
0.0024169084364
 
< 0.1%
0.0024270968733
 
< 0.1%
ValueCountFrequency (%)
0.51062626191
 
< 0.1%
0.51024367481
 
< 0.1%
0.50887560261
 
< 0.1%
0.50846890882
 
< 0.1%
0.50846890885
< 0.1%
0.50687438871
 
< 0.1%
0.50655035711
 
< 0.1%
0.5057743461
 
< 0.1%
0.50467384531
 
< 0.1%
0.50385866641
 
< 0.1%

y
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2.0
356435 
0.0
210580 
1.0
193537 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2281656
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0356435
46.9%
0.0210580
27.7%
1.0193537
25.4%

Length

2022-11-02T16:58:00.466361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T16:58:00.575745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0356435
46.9%
0.0210580
27.7%
1.0193537
25.4%

Most occurring characters

ValueCountFrequency (%)
0971132
42.6%
.760552
33.3%
2356435
 
15.6%
1193537
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1521104
66.7%
Other Punctuation760552
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0971132
63.8%
2356435
 
23.4%
1193537
 
12.7%
Other Punctuation
ValueCountFrequency (%)
.760552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2281656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0971132
42.6%
.760552
33.3%
2356435
 
15.6%
1193537
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2281656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0971132
42.6%
.760552
33.3%
2356435
 
15.6%
1193537
 
8.5%

Interactions

2022-11-02T16:57:51.765252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:17.677278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:21.051934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:24.793155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:28.095601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:31.452998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:34.729486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:37.948388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:41.430866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:45.013602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:48.428725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:52.053119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:18.004461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:21.351020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:25.085224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:28.404048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:31.795500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:35.014088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:38.235269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:41.768809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:45.331091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:48.744500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:52.335156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:18.301871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:21.700863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:25.371759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:28.735563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:32.089223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:35.317410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:38.550431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:42.062428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:45.648964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:49.031952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:52.635141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:18.614474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:22.000782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:25.682337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:29.031841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:32.366661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:35.618077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:38.832765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:42.368999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:45.969155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:49.315496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:52.933316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:18.917236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:22.299030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:25.973285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:29.314065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:32.667904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:35.903091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:39.115491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:42.699681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:46.289495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:49.664082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:53.213575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:19.198807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:22.606337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:26.268703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:29.629740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:32.952134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:36.185504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:39.415388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:43.051700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:46.600457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:49.951538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:53.517649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:19.520213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:22.906183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:26.580031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:29.931728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:33.248792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:36.480546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:39.961602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:43.371336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:46.935513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:50.249675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:53.818665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:19.834537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:23.204255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:26.884221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:30.233944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:33.534568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:36.800465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:40.250696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:43.760422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:47.225415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:50.552111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:54.101171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:20.137817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:23.520180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:27.177767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:30.536286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:33.833687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:37.067768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:40.555308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:44.077905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:47.530587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:50.849466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:54.400872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:20.435059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:23.827036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:27.480404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:30.836072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:34.116150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:37.367279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:40.835603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:44.367872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:47.833028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:51.147949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:54.715950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:20.767139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:24.115760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:27.802432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:31.136023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:34.404076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:37.664136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:41.117500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:44.716631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:48.133169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T16:57:51.429148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T16:58:00.669472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T16:58:00.856930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T16:58:01.028725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T16:58:01.466164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T16:57:55.157685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T16:57:55.752334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_stdpacket_loss_ratepacket_loss_stdy
00.7448240.0255120.7869080.0139180.0032300.0000000.003230.0661470.0103900.0002500.0000001.0
10.7448240.0255120.8101220.0558030.0032300.0000000.003230.0770220.0417970.0314920.1530551.0
20.7344080.0765370.7916700.0317810.0032300.0000000.003230.0691720.0170700.0002500.0000001.0
30.7500310.0000000.8261930.0155730.0032300.0000000.003230.0617030.0212210.0031210.0140671.0
40.7031620.1598560.8166690.0054380.0032300.0000000.003230.0409570.0628980.0031210.0140671.0
50.5938010.2188200.8220260.0094000.0032300.0000000.003230.2521410.3005380.0059930.0194561.0
60.7031620.1943690.8238120.0143890.0032300.0000000.003230.2174410.1887490.0239490.1023941.0
70.6875390.1805560.8202410.0118530.0032300.0000000.003230.1410070.2413650.0002500.0000001.0
80.6406700.2029170.8279790.0122670.0032300.0000000.003230.2485040.2344090.0419060.2040731.0
90.7081770.0861210.8267880.0087480.0862940.2814181.000000.3985410.3060680.0031210.0140671.0

Last rows

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_stdpacket_loss_ratepacket_loss_stdy
7605420.8083720.0408170.5326640.0905561.00.01.00.3284510.2142371.00.01.0
7605430.8000400.0000000.5130670.0024731.00.01.00.3098410.1689971.00.01.0
7605440.8000400.0000000.5286040.0715851.00.01.00.4150250.1229761.00.01.0
7605450.8000400.0000000.5141260.0026991.00.01.00.3532000.2265851.00.01.0
7605460.8000400.0000000.5141880.0024271.00.01.00.4614400.2548321.00.01.0
7605470.7833770.0564550.5153620.0030121.00.01.00.3733300.1603781.00.02.0
7605480.8000400.0000000.1294180.2289731.00.01.00.4753730.1061551.00.02.0
7605490.8000400.0000000.0000040.0000001.00.01.00.5049830.0124171.00.02.0
7605500.8000400.0000000.0000040.0000001.00.01.00.5061180.0117331.00.02.0
7605510.8000400.0000000.0000040.0000001.00.01.00.5044970.0156251.00.02.0

Duplicate rows

Most frequently occurring

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_stdpacket_loss_ratepacket_loss_stdy# duplicates
11601.0000000.01.0000000.01.0000000.01.0000001.000000e+000.01.0000000.01.0246
7920.6666760.00.3372820.00.0000830.00.0000832.505194e-010.00.0000910.00.098
10660.8823600.00.0566220.00.0025560.00.0025563.792424e-010.00.1161620.02.082
5070.5000620.00.6250160.00.0295240.00.0295244.716786e-010.00.5192310.01.074
6570.5714900.00.2016270.00.0131810.00.0131811.673281e-010.00.0003330.00.071
11211.0000000.00.0244350.00.0018540.00.0018541.000000e+000.00.0228650.02.047
9200.7272980.00.2353390.01.0000000.01.0000001.784937e-010.00.1181660.00.038
2280.2858160.00.0900980.01.0000000.01.0000002.260037e-010.01.0000000.00.036
650.0000500.00.5882600.00.0700670.00.0700672.890006e-080.00.0002000.00.029
2080.2667160.00.2638990.00.0224070.00.0224078.901202e-020.00.0001670.00.029